I recently started working as a Data Scientist and here are my thoughts after the first three months into my job. Apart from these small learnings there are also a lot of technical aspects which I came across but that anyone can learn even after joining.
Machine Learning versus economic value- In school we take courses on different learning algorithms but not on their economic value. It is important to understand accuracy is not the king, budget is! We need to know the use cases where logistic regression is being used in Million dollar industries, how a simple model can…
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I have been doing a lot of reading around successful AI products! With the recent acquisition of Nuance by Microsoft for $19.7 billion, there is no better time to dive into the Artificial Intelligence space and build something of your own. As Elon Musk said ~
I could either watch it happen or be a part of it :)
I brainstormed a list of ideas which anyone could immediately start working on. These can be started small but have the potential of having a great impact. Then the question comes why am I sharing with everyone? Because I cannot work…
I recently interviewed with Microsoft (Data Scientist ll), Amazon (Applied AI Scientist) and Apple (Software Development : Machine Learning). I wrote about the preparation involved on the Machine Learning side and received a lot of questions about coding questions in interviews.
These are the major types of questions I was asked. Obviously the difficulty will varying according to the team and the company. Feel free to add more questions!
These are my favorite types of tests because you can actually showcase your skills. Here are some of the types of questions I have been asked
Preparing for your interviews is one aspect of job search and getting a job that you want is another aspect of it. I wrote about the first part recently which makes this post more important. I have seen so many people struggling to take decisions between which job to take. Since most companies cannot talk about the actual work they are doing beforehand, it's difficult to get to know what exactly does the team work on and what your life would look like after joining the company.
I know the question: “So do you have any questions for me now”…
Deep learning has evolved from just being parameterized non linear functions to being used in major computer vision and natural language processing tasks. The piecewise non linear networks are able to form non trivial representations of data. Though, these networks have been highly successful there are many gaps between their understanding of why they perform so well by finding near optimal solutions to problems.
These models give 0 training error, therefore highly overfitting the training data but are still able to give good test performance.
This benign overfitting appears to contradict accepted statistical wisdom, which insists on a trade-off between…
The advances in ‘Artificial Intelligence’ can be directly mapped to the progress in Deep Learning. With the exponential growth in technological advancements and papers, it becomes inevitable to understand- What’s so special about the field. Scientists have been building models before deep learning then Why does deep learning work: So well?
Medium has been flooded with the applications of language processing and computer vision. But I believe it lacks in introductory theory articles. This brings a necessity to write more about why deep learning works, what is the research in ‘why’ and ‘how’ it works.
This is a series of…
For me personally, I love newsletters which give a concise overview weekly/ daily.
I have signed up for Sebastian Ruder's NLP newsletter, The hustle (https://thehustle.co/) daily newsletter.
One of the major sources for me is medium where on TDS I come across interesting ideas and topics everyday.
I usually spend my Sunday mornings catching up on Machine Learning papers or new technology. Something really exciting caught my eye. Google’s creativity lab open sourced project Alto!
It is a toolkit to teach developers how they can incorporate machine learning functionality in their next hardware project. They mention this as ‘a little teachable object’.
It is basically a software to be integrated with your home bought hardware. It is connected with the Coral USB Accelerator at the backend, therefore giving the capabilities of object recognition, segmentation, voice recognition and so on. …
It’s true that online learning gives you the flexibility and affordability to take up whatever courses you like. But there are several reasons I think a formal degree can help someone aspiring to enter the field of Data Science.
This is just my own perspective and people can disagree with me on many levels. Feel free to share your own points.
Let’s say you want to learn machine learning. You start up with Andrew Ng’s Machine Learning course and finish it. Now you Google what else can you study in ML. You will find 100’s of ’10 best Machine Learning…
Data Science @Microsoft security research | Editorial Associate Towards Data Science(TDS)